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lifetime

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5 Hours

Linear Regression is a powerful method for quantifying the cause and effect relationships that affect different phenomena in the world around us. This course will teach you how to build robust linear models that will stand up to scrutiny when you apply them to real world situations. You'll even put what you've learnt into practice by leveraging Excel, R, and Python to build a model for stock returns.

- Access 40 lectures & 5 hours of content 24/7
- Cover method of least squares, explaining variance, & forecasting an outcome
- Explore residuals & assumptions about residuals
- Implement simple & multiple regression in Excel, R, & Python
- Interpret regression results & avoid common pitfalls
- Introduce a categorical variable

Loonycorn is comprised of four individuals—Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh—who have honed their tech expertises at Google and Flipkart. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Details & Requirements

- Length of time users can access this course: lifetime
- Access options: web streaming, mobile streaming
- Certification of completion not included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels

Compatibility

- Internet required

Terms

- Instant digital redemption

- Introduction
- You, This Course and Us (1:54)

- Connect the Dots with Linear Regression
- Using Linear Regression to Connect the Dots (9:04)
- Two Common Applications of Regression (5:24)
- Extending Linear Regression to Fit Non-linear Relationships (2:36)

- Basic Statistics Used for Regression
- Understanding Mean and Variance (6:03)
- Understanding Random Variables (16:54)
- The Normal Distribution (9:31)

- Simple Regression
- Setting up a Regression Problem (11:36)
- Using Simple regression to Explain Cause-Effect Relationships (4:57)
- Using Simple regression for Explaining Variance (8:07)
- Using Simple regression for Prediction (4:04)
- Interpreting the results of a Regression (7:25)
- Mitigating Risks in Simple Regression (7:56)

- Applying Simple Regression Using Excel
- Applying Simple Regression in Excel (11:57)
- Applying Simple Regression in R (11:14)
- Applying Simple Regression in Python (6:05)

- Multiple Regression
- Introducing Multiple Regression (7:03)
- Some Risks inherent to Multiple Regression (10:06)
- Benefits of Multiple Regression (3:48)
- Introducing Categorical Variables (6:58)
- Interpreting Regression results - Adjusted R-squared (7:02)
- Interpreting Regression results - Standard Errors of Co-efficients (8:12)
- Interpreting Regression results - t-statistics and p-values (5:32)
- Interpreting Regression results - F-Statistic (2:52)

- Applying Multiple Regression using Excel
- Implementing Multiple Regression in Excel (8:54)
- Implementing Multiple Regression in R (6:26)
- Implementing Multiple Regression in Python (4:21)

- Logistic Regression for Categorical Dependent Variables
- Understanding the need for Logistic Regression (9:24)
- Setting up a Logistic Regression problem (6:02)
- Applications of Logistic Regression (9:55)
- The link between Linear and Logistic Regression (8:13)
- The link between Logistic Regression and Machine Learning (4:16)

- Solving Logistic Regression
- Understanding the intuition behind Logistic Regression and the S-curve (6:21)
- Solving Logistic Regression using Maximum Likelihood Estimation (10:02)
- Solving Logistic Regression using Linear Regression (5:32)
- Binomial vs Multinomial Logistic Regression (4:04)

- Applying Logistic Regression
- Predict Stock Price movements using Logistic Regression in Excel (9:52)
- Predict Stock Price movements using Logistic Regression in R (8:00)
- Predict Stock Price movements using Rule-based and Linear Regression (6:44)
- Predict Stock Price movements using Logistic Regression in Python (4:49)

access

lifetime

content

5 Hours